Christa's GIS Archive

It's the last leg of the race to the end, Module 5 - Part 2. This last part of Module 5 consisted of the corridor analysis , which showcased the migration path of black bears between two sections of Coronado National Park. Several tools were utilized, similar to the first Module 5 blog post. Reclassify was used to get our values where we needed them based on the provided suitability values. To note, we had to ensure the rasters were set to a 30cm cell size. Then, we applied the Weighted Overlay tool to specify percentages for each raster (landcover was 60% while elevation and roads distance were set to 20%), followed by the Raster Calculator tool and inverting the values to prepare for the final corridor tool, which accounted for the Coronado polygon layers . The map below represents my final results. Coronado National Park Black Bear Movement. A corridor analysis of the travel pathways black bears are likely to take based on roads, elevation, and land cover. Overall, I do wish I h...

We're starting the last Module 5 on Suitability and Least Cost analysis, where we walk through several hypothetical situations to identify the best-suited conditions for construction as well as to identify conservation zones. I started these last labs with high hopes of finishing at a decent time, so I wouldn't be juggling work, classes, and traveling -- but alas, this colossal titan of a final lab separated into 4 parts/scenarios got to me and has followed me out of state. Haha! For this first half of the lab, we created two maps: equal-weighting and slope-priority, which offer different perspectives for potential construction in Jackson County, Oregon. We reclassified several rasters: landcover, soils, slope, roads, and rivers. We then used the Weighted Overlay tool to set values in a range from 1-5, with all 5 layers weighted equally at 20% each for the equal-weighted map. Afterward, we applied alternative weight values to each of the previous 5 layers, which were  more sl...

We're back again on the East Coast, specifically in New Jersey, to continue where we left off. This week, we're building on Module 3's flooding assessment with Module 4: Coastal Damage Assessment. Thankfully, this lab did not throw me into a whirlwind in comparison to last week -- things went very smoothly! Module 4 consisted of 3 parts: tracking Hurricane Sandy from its formation and pathway until it made landfall, a Survey123 damage assessment form, and a case study in New Jersey's local community where structural damage was analyzed. Part 1 provided background on Hurricane Sandy. We prepared point and polyline data of the storm to track its pathway. This part brought me back to the Cartography course thanks to its section on symbology and labeling. I felt confident with this part since I had already made a fair amount of custom icon points for the choropleth map of wine consumption in Europe, where I created a unique grape icon from scratch. It's fulfilling to ha...

Ah, summer. How I wish to be at the beach! It's been raining on and off with this high-temperature weather. Sadly, no vacation until the end of the month, so I'm holding out and trying to get these modules done as early as I can. Instead, I get the chance to learn about the coast rather than be at the coast. Module 3 is on Coastal Flooding. This time around, we're talking about New Jersey and Florida, with Part A & B covering Hurricane Sandy and storm surges across New Jersey while Part C brings us back to Florida. This lab required a lot of LiDAR and DEM work, so I had to make sure I followed instructions way more than last week. Out of the gate, I was saddened because it required us to have access to the Data Interoperability Extension, which my laptop lacks. It drove me up the wall having to go back into the virtual desktop, but thankfully, Part A was the only part that required this and the SpatialETL Tool. It allowed us to convert LAZ to LAS. From there, we generat...

Light Detection and Ranging (LiDAR) is a powerful remote sensing technology that enables us to map the world more precisely with high-resolution models. It is also this week's Module topic! I'm not new to LiDAR or to working with LiDAR/DEMs; however, the application we do is different from what I experienced in Module 2, which is great! I love the variety and change of pace. It really goes to show how versatile GIS is. This week, we find ourselves in Virginia, specifically Shenandoah National Park. It is there that we're given a file geodatabase with Virginia boundaries in polygon and polyline form, as well as a LAZ file, which is a compressed version of LiDAR point cloud data. I immediately learned that LAZ files are not readable in ArcGIS Pro, while LAS files are. After some trial and error, I realized I didn't need to run the Convert LAS tool on the provided data, since it was already in the proper format for ArcPro to read. Simply misunderstood the instructions that...

Getting the ball rolling again after a nice vacation has been hard, but I'm doing my best to stay on top of things, especially with a few extra days off thanks to the July 4th weekend. To dive right into the swing of things, it's the next course on Applications in GIS, featuring the first Module on Crime Analysis. The work I do doesn't f ocus on identifying and analyzing hotspots, so it was a nice change of pace to learn and work through the process of creating different types of hotspot maps and how such data can assist in crime prediction. The three main types of hotspot analyses that were covered were Grid Overlay, Kernel Density, and Local Moran's I. The two main datasets we worked with were in Washington, DC and Chicago, Illinois. Washington, DC's data helped us get a foundation for learning how to prepare burglary rates in a choropleth map. Then, we created kernel density hotspots in DC as well. From there, we went to Chicago and created 3 hotspot maps: Grid, ...